Triple

T6327967
Position Surface form Disambiguated ID Type / Status
Subject Difference Engine E141904 entity
Predicate errorReductionGoal P70875 FINISHED
Object eliminate human errors in table calculation LITERAL FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: eliminate human errors in table calculation | Statement: [Difference Engine, errorReductionGoal, eliminate human errors in table calculation]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: errorReductionGoal
Context triple: [Difference Engine, errorReductionGoal, eliminate human errors in table calculation]
  • A. noiseReductionGoal
    Indicates the intended target level or objective for reducing noise in a given context or system.
  • B. oversightGoal
    Indicates that an entity has the purpose or objective of overseeing, supervising, or monitoring another entity, process, or activity.
  • C. aimedToLimit
    Indicates an action or policy that was intentionally directed toward restricting, reducing, or constraining something.
  • D. restorationGoal
    Indicates that an action or plan is intended to return something to a previous, original, or improved state or condition.
  • E. safetyGoal
    Indicates that an entity is associated with a specific safety objective or target condition intended to prevent harm or reduce risk.
  • F. None of above. chosen

Provenance (4 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69c008d201748190917e69c41ba3f978 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c064e9532081908277f10ec380a486 completed March 22, 2026, 9:53 p.m.
PD Predicate disambiguation batch_69c060e7e2d48190af9d004236466788 completed March 22, 2026, 9:36 p.m.
PDg Predicate description generation batch_69c064c080148190a7c3218867f1f572 completed March 22, 2026, 9:53 p.m.
Created at: March 22, 2026, 4:29 p.m.